A learning scheme for a fuzzy k-NN rule

被引：75

作者：

Jozwik, Adam ^{[1
]}

机构：

[1] Polish Acad Sci, Inst Biocybernet & Biomed Engn, PL-00818 Warsaw, Poland

来源：

PATTERN RECOGNITION LETTERS | 1983年 / 1卷 / 5-6期

关键词：

NN rules; learning procedure; fuzzy decisions; probability of misclassification;

D O I：

10.1016/0167-8655(83)90064-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The performance of a fuzzy k-NN rule depends on the number k and a fuzzy membership- array W[l, m(R)], where l and m(R) denote the number of classes and the number of elements in the reference set X-R respectively. The proposed learning procedure consists in iterative finding such k and W which minimize the error rate estimated by the 'leaving one out' method.

引用

页码：287 / 289

页数：3